Brand Visibility in AI Search Requires Structured, Answer-Ready Content

In a landscape rapidly reshaped by AI, Andrew Lederman of Mondelez offers a critical strategic blueprint for brands navigating the seismic shift in digital commerce. This conversation reveals not just the technical adjustments required to appear in AI search results, but the deeper, non-obvious consequences of this transition. Brands that fail to adapt risk becoming invisible, not just to consumers, but to the very systems that will soon mediate most purchasing decisions. This analysis is essential for marketers, strategists, and anyone invested in understanding how to maintain relevance and competitive advantage in an increasingly agentic future, providing a clear roadmap to prepare for a future where AI doesn't just recommend, but recommends and transacts.

The Hidden Cost of Visibility: Why Blocking Bots Was a Brand's First Mistake

The initial impulse for many brands, including Mondelez, was to block AI crawlers from their websites. This was a defensive posture, born from a desire to control information and protect proprietary data. However, as Andrew Lederman explains, this strategy was fundamentally flawed. By blocking these bots, brands like Oreo were effectively rendering themselves invisible to the emerging AI search engines, leading to a mere 10% citation rate in AI responses. The immediate consequence was a loss of visibility, but the downstream effect was far more profound: a gradual erosion of brand relevance in the very channels that are poised to dominate consumer discovery.

Lederman frames this shift as a necessary evolution from an "impression-based thinking" to a "citation-based influence." The old metrics of reach and visibility are no longer sufficient. AI systems, he notes, require structured, factual, and "answer-ready" content. This means moving beyond traditional marketing collateral to create content that AI models can easily parse and quote. The challenge lies in translating the deep, emotional resonance of brands like Oreo into structured data that AI can understand and recommend. This requires a fundamental rethinking of content creation, shifting focus from broad emotional appeals to specific, answerable formats like FAQs, comparison pages, and detailed occasion-based content.

"We actually have to fully take a different look at our brand.com experiences and make sure that they're optimized towards AI. And this is not something that we had previously done."

-- Andrew Lederman

The implication is that brands must invest in building a "knowledge infrastructure for AI." This involves not just unblocking crawlers, but actively structuring product knowledge consistently across all surfaces--retailer sites, brand sites, and earned media. This holistic approach ensures that AI systems can accurately interpret and trust brand information. The delayed payoff here is significant: brands that invest in this structured knowledge base now will build a durable competitive advantage, becoming the trusted sources that AI agents consistently recommend, while others struggle to catch up.

The Unseen Complexity of "Answerability": More Than Just Content

The pursuit of "answerability" for AI systems is more complex than simply producing more content. Lederman breaks down Mondelez's approach into three key technological pillars: accessibility, structure, and answerability. While unblocking crawlers addresses accessibility, the real work lies in structuring content so that AI models can parse it effectively, and then reshaping that content to be directly answerable.

This requires a granular approach to on-site optimization. Clean sitemaps, proper TXT files, and fast load times are foundational. But beyond that, brands must implement schema at scale, create comprehensive FAQs, and use clear H1s and strong introductory paragraphs. The concept of "TLDR" summaries, which are common in human-readable content, is now being applied to AI-optimized content to provide AI models with concise takeaways. Formats like FAQs and comparison pages are particularly valuable because they offer direct answers that LLMs can easily quote.

The downstream consequence of neglecting this structured approach is that even if a brand's content is accessible, it may be too fragmented or poorly organized for AI to effectively utilize. Lederman highlights that a single AI-generated answer can pull from 30 different citation sources. If a brand's owned and earned media are not structured to be easily digestible, they risk being overlooked in favor of more organized competitors. This creates a competitive moat for those who invest in this technical rigor, as it becomes a significant barrier to entry for less sophisticated players.

"We ensure consistent structured product knowledge across every surface. So retailer PDPs, brand sites, and earned media, so that AI systems can accurately interpret and trust our brands."

-- Andrew Lederman

Furthermore, the shift from traditional marketing to AI-optimized content necessitates building new internal capabilities. Brands accustomed to creating emotional video commercials must now develop a "muscle" for long-form text content. This requires not only new skills but potentially organizational restructuring. Lederman muses about consolidating digital content under a single department to ensure a unified approach, where content briefs generate assets for multiple platforms simultaneously. This organizational agility, while challenging to implement, promises a significant advantage in efficiently producing the volume and quality of content required for AI visibility.

The Delayed Payoff of Agentic Commerce: Preparing for a Transactional Future

The most significant, and perhaps most daunting, aspect of the AI shift is the rise of agentic commerce--where AI agents not only recommend but also transact. Mondelez is preparing for a future where 20-30% of their digital commerce volume could be made via AI agents within the next year to year and a half. This is not a question of "if," but "when." The immediate challenge is ensuring their products are recommended by these LLMs. The transaction itself, whether through an instant checkout within an LLM or a referral to a retailer, is secondary to securing that initial recommendation.

This focus on recommendation is where the delayed payoff truly lies. Brands that invest now in structured data, answer-ready content, and consistent product knowledge will be positioned to win when agentic commerce becomes mainstream. The effort required to build this knowledge infrastructure is substantial and may not yield immediate, visible results. This is precisely why it creates a competitive advantage: most organizations lack the patience or foresight to undertake such foundational work.

"We have no expectations of agentic commerce increasing the pace in which digital commerce adoption is going. But of those who have adopted digital commerce, the odds of them using LLMs is quite high."

-- Andrew Lederman

Lederman emphasizes that while timelines are debated (base, bull, and bear cases), the substantial shift in digital business is undeniable. The preparation involves translating the human understanding of brands like Oreo--their nostalgia, their emotional resonance--into data that AI can comprehend. This is a complex task, requiring the right structured data, content aligned with consumer occasions, and holistic operations to tell a cohesive story. By focusing on this recommendation piece now, Mondelez is building a capability that will pay dividends for years to come, ensuring they are not just present but preferred in the future of commerce.

Key Action Items

• Immediate Action (0-3 Months):
  • Audit current website content for AI crawlability and "answerability." Identify immediate gaps in structured data, schema implementation, and clear H1s/intro paragraphs.
  • Unblock all AI crawlers across owned digital properties. Review and clean up sitemaps and robots.txt files to ensure smooth access.
  • Initiate cross-functional alignment meetings between SEO, paid media, content, and digital commerce teams to discuss AI strategy.
• Short-Term Investment (3-9 Months):
  • Develop and implement a scalable schema strategy for product pages, FAQs, and comparison content across brand.com and key retailer sites.
  • Begin producing long-form, answer-ready content focused on specific consumer occasions relevant to your brands (e.g., holiday baking, energy needs for athletes).
  • Establish a measurement framework with at least five KPIs for AI visibility, citation, and sentiment. Select and begin using 1-2 AI tracking tools.
• Medium-Term Investment (9-18 Months):
  • Explore and pilot advertising or sponsored content opportunities within emerging LLM platforms and retailer-specific AI tools (e.g., Rufus, Spotify).
  • Evaluate organizational structure for digital content creation. Consider consolidating efforts to ensure consistent messaging across owned, earned, and retail channels.
  • Refine AI content strategy based on initial performance data, focusing on occasions where market share and share of voice are misaligned. This investment builds a durable moat, ensuring brand relevance in the evolving agentic commerce landscape.